CN116990707A - Battery life prediction method and device, electronic equipment and storage medium - Google Patents

Abstract

The embodiment of the invention discloses a battery life prediction method, a battery life prediction device, electronic equipment and a storage medium. The battery life prediction method comprises the following steps: simulating aging state curves corresponding to the battery to be estimated under different environment temperatures and storage time by using the aging memory effect of the battery to be estimated; splicing the aging state curves according to time as a splicing basis to obtain final aging state curves; and obtaining a life prediction result of the battery to be predicted according to the final aging state curve. The invention can more accurately estimate the service life of the battery and meet the high-precision estimating requirement.

Description

Battery life prediction method, device, electronic equipment and storage medium

Technical field

Embodiments of the present invention relate to the field of battery technology, and in particular, to a battery life prediction method, device, electronic equipment and storage medium.

Background technique

Battery life capability evaluation is no longer limited to the pure cycle/calendar life capability of the battery, but has shifted more to the battery life capability in actual scenarios. Battery warranty requirements and service life are based on specific regions and specific working conditions (common: a certain region - 8 years and 200,000 kilometers). This puts higher requirements on battery life prediction work.

Predict battery life based on actual scenarios, when boundary conditions change frequently, for example, under a uniform preset temperature distribution, the battery is periodically operated and shelved. According to the traditional life prediction method, the equivalent temperature/equivalent SOC method is adopted to simplify complex working conditions into a single scenario for prediction and evaluation of battery life. This obviously deviates from the application scenario and cannot obtain an accurate estimate of battery life. As a result, it cannot meet the demand for high-precision prediction.

Contents of the invention

The present invention provides a battery life prediction method, device, electronic equipment and storage medium, which can predict battery life more accurately and meet high-precision prediction requirements.

According to one aspect of the present invention, a battery life prediction method is provided. The battery life prediction method includes:

Utilize the aging memory effect of the battery to be estimated to simulate the aging state curve corresponding to the battery to be estimated under different ambient temperatures and storage times;

The aging state curve is spliced according to time as the splicing basis to obtain the final aging state curve;

The life prediction result of the battery to be estimated is obtained according to the final aging state curve.

Optionally, using the aging memory effect of the battery to be estimated to simulate the aging state curve corresponding to the battery to be estimated under different conditions includes:

Put the battery to be estimated at the first ambient temperature for a first storage time to simulate a first aging state curve;

The battery to be estimated is placed at a second ambient temperature for a second storage time to simulate a second aging state curve.

Optionally, splicing the aging state curve according to time as a splicing basis to obtain the final aging state curve includes:

The end of the first aging state curve is spliced to the starting end of the second aging state curve to obtain a time-continuous final aging state curve.

Optionally, obtaining the life prediction result of the battery to be estimated based on the final aging state curve includes:

According to the aging state value corresponding to the end of the final aging state curve successively in time, the sum of the attenuation amounts of the battery to be estimated is obtained, thereby obtaining the life prediction result of the battery to be estimated.

Optionally, the aging state value at the end of the first aging state curve is equal to the aging state value at the beginning of the second aging state curve, and the aging state of the battery to be estimated corresponding to the same aging state value is Are not the same.

Optionally, the influencing factors of the aging memory effect of the battery to be estimated include: storage life or cycle life.

Optionally, the ambient temperature includes 25°C-60°C, and the storage time is 4 hours.

According to another aspect of the present invention, a battery life prediction device is provided. The battery life prediction device includes:

The aging state curve simulation module is used to utilize the aging memory effect of the battery to be estimated and simulate the aging state curve corresponding to the battery to be estimated under different ambient temperatures and storage times;

An aging state curve splicing module is used to splice the aging state curve according to time as a splicing basis to obtain the final aging state curve;

The battery life prediction module to be estimated is used to obtain the life prediction result of the battery to be estimated based on the final aging state curve.

According to another aspect of the present invention, an electronic device is also provided. The electronic device includes:

one or more processors;

Memory, used to store one or more programs;

When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method described in the embodiment of the present invention.

According to another aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored. When the computer program is executed by a processor, the method as described in the embodiment of the present invention is implemented.

The technical solution of the embodiment of the present invention uses the aging memory effect of the battery to be estimated to simulate the aging state curve corresponding to the battery to be estimated under different ambient temperatures and storage times; the aging state curve is spliced according to time as the splicing basis to obtain the final result. Aging state curve; obtain the life prediction result of the battery to be estimated based on the final aging state curve; approximately simulate the life aging behavior of the battery in the actual working scenario, realize the coupling of working conditions in different scenarios, and restore the aging situation of the battery in real operation. The aging behavior at any time is consistent with the objective time, ultimately achieving high-precision life prediction of battery life. To sum up, the present invention solves the problem that the battery life prediction in the prior art deviates from the application scenario, cannot obtain accurate results of estimating battery life, and cannot meet the demand for high-precision prediction.

It should be understood that what is described in this section is not intended to identify key or important features of the embodiments of the invention, nor is it intended to limit the scope of the invention. Other features of the present invention will become easily understood from the following description.

Description of the drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without exerting creative efforts.

Figure 1 is a flow chart of a battery life prediction method provided according to an embodiment of the present invention;

Figure 2 is a schematic diagram of the aging curve of an existing battery at different temperatures;

Figure 3 is a schematic diagram of the splicing principle of an aging state curve provided according to an embodiment of the present invention;

Figure 4 is a schematic diagram of the splicing principle of the aging state curve in an actual application scenario according to an embodiment of the present invention;

Figure 5 is a schematic structural diagram of a battery life prediction device provided according to an embodiment of the present invention;

FIG. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed ways

In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only These are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts should fall within the scope of protection of the present invention.

It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances so that the embodiments of the invention described herein are capable of being practiced in sequences other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus that encompasses a series of steps or units and need not be limited to those explicitly listed. Those steps or elements may instead include other steps or elements not expressly listed or inherent to the process, method, product or apparatus.

Figure 1 is a flow chart of a battery life prediction method according to an embodiment of the present invention. Referring to Figure 1, an embodiment of the present invention provides a battery life prediction method. The battery life prediction method can be executed by a battery life prediction device. The battery life prediction device can be integrated into an electronic device, and the battery life prediction device can be implemented by software and/or hardware. The battery life prediction method includes the following steps:

S110. Use the aging memory effect of the battery to be estimated to simulate the aging state curve corresponding to the battery to be estimated under different ambient temperatures and storage times.

Specifically, Figure 2 is a schematic diagram of the aging curve of an existing battery at different temperatures. Referring to Figure 2, the aging memory effect means that battery aging is not only related to the environmental stress of the current state, but also affected by the aging path. Among them, environmental stress includes temperature, current, and state of charge (State of Charge, SOC). The aging path refers to the change in the battery application scenario. For example, aging path one: the battery first works at 25°C for 1 hour, and then works at 45°C for one hour; aging path two: the battery first works at 45°C for 1 hour, and then works at 25°C for one hour. Hour. As shown in Figure 2: a 65°C cycle will accelerate the aging effect of the subsequent 25°C cycle.

Single operating condition life capability refers to the pure cycle/pure calendar life capability of the battery, for example, 25℃, 0.5C0.5C (0.5C refers to the current value of battery charging and discharging); 100% DOD cycle for 1500 weeks to reach 80% battery health (State of Health, SOH); stored at 45°C and 80% SOC for 1000 days to reach 80% SOH. Among them, battery health can be understood as the percentage of the battery's current capacity and the factory capacity.

The battery's scenario life capability refers to the battery's life capability in actual application scenarios. The actual operating conditions of the Battery Management System (BMS) include cycling and shelving alternately.

For example, the battery is stored at ambient temperature for a period of time to simulate the first aging state curve of the battery; and then the ambient temperature is changed to a preset temperature and stored for a period of time to simulate the second aging state curve of the battery.

S120. Splice the aging state curves corresponding to the battery to be estimated under different conditions according to time as the splicing basis to obtain the final aging state curve.

S130. Obtain the life prediction result of the battery to be estimated according to the final aging state curve.

Specifically, the simulated first aging state curve and the second aging state curve of the battery are spliced according to one-way, objective time as the splicing basis, and finally the aging state curve of the battery to be estimated is obtained, through which the curve can be intuitively Make predictions about battery life. By allowing the battery to be estimated to be estimated under different ambient temperatures and different storage times, the battery life is estimated. Instead of using a single temperature or SOC to predict the battery life, the real environment of the battery is more objectively restored, making it more accurate. of estimated battery life.

The technical solution of the embodiment of the present invention uses the aging memory effect of the battery to be estimated to simulate the aging state curve corresponding to the battery to be estimated under different ambient temperatures and storage times; the aging state curve is spliced according to time as the splicing basis to obtain the final result. Aging state curve; obtain the life prediction result of the battery to be estimated based on the final aging state curve; approximately simulate the life aging behavior of the battery in the actual working scenario, realize the coupling of working conditions in different scenarios, and restore the aging situation of the battery in real operation. The aging behavior at any time is consistent with the objective time, ultimately achieving high-precision life prediction of battery life. To sum up, the present invention solves the problem that the battery life prediction in the prior art deviates from the application scenario, cannot obtain accurate results of estimating battery life, and cannot meet the demand for high-precision prediction.

Figure 3 is a schematic diagram of the splicing principle of an aging state curve according to an embodiment of the present invention. Referring to Figure 3, optionally, the aging memory effect of the battery to be estimated is used to simulate the corresponding aging state of the battery to be estimated under different conditions. The curve includes: putting the battery to be estimated at the first ambient temperature and shelving it according to the first storage time to simulate the first aging state curve; putting the battery to be estimated at the second ambient temperature and shelving it according to the second storage time. , simulate the second aging state curve.

Specifically, referring to Figure 3A, in the hypothetical scenario, the battery is first stored at the first ambient temperature (60°C) for the first storage time Δt1, and the first aging state curve SOH1 is simulated; and then the first ambient temperature is changed to the second The ambient temperature (35°C) continues to be stored according to the second storage time Δt2, and the second aging state curve SOH2 is simulated.

Continuing to refer to Figure 3, optionally, splicing the aging state curve according to time as the splicing basis to obtain the final aging state curve includes:

The end of the first aging state curve is spliced to the beginning of the second aging state curve to obtain a final aging state curve that continues over time.

Continuing to refer to Figure 3, optionally, obtaining the life prediction result of the battery to be estimated based on the final aging state curve includes:

According to the aging state value corresponding to the end of the final aging state curve in time succession, the sum of the attenuation of the battery to be estimated is obtained, thereby obtaining the life prediction result of the battery to be estimated.

Specifically, referring to Figure 3B, the first ambient temperature is 60°C and Δt1 is stored, and the attenuation is ΔQloss1. Find the point on the 35°C curve that is the same time (Time) as the 60°C-Δt1 end state, and then use this as the starting point of the 35°C attenuation. From the initial state, continue to attenuate Δt2 to obtain ΔQloss2. The overall attenuation of the entire process can be described as ΔQloss1+ΔQloss2. The advantage of this method is that it can correspond to the actual scene in time.

Continuing to refer to Figure 3, optionally, the aging state value at the end of the first aging state curve SOH1 is equal to the aging state value at the starting end SOH2 of the second aging state curve. The aging state of the battery to be estimated corresponding to the same aging state value is not the same. same.

Specifically, this embodiment does not follow the SOH connection adopted in the prior art, but prefers to select a one-way, objective "time" as the basis for splicing.

Since the aging process affects the battery state (aging memory effect), the same SOH does not mean the same battery aging state. Batteries go through different aging pathways. Even if they reach the same SOH, their internal aging states are different. It can correspond to the actual scene one-to-one in time, realize the coupling of different scene working conditions, and achieve high-precision life prediction.

Figure 4 is a schematic diagram of the splicing principle of the aging state curve in an actual application scenario according to an embodiment of the present invention. Referring to Figure 4, optionally, the influencing factors of the aging memory effect of the battery to be estimated include: storage life or cycle life.

Specifically, in actual battery application scenarios, the interaction between storage life or cycle life must also be considered. The working scenario shown in Figure 4: The battery is first placed at 25°C for 4 hours, then worked at 35°C for 4 hours, then adjusted to 45°C for 4 hours, and finally worked at 45°C for 4 hours. This cycle of operations continues until the battery life declines to the end of life (EOL).

a in Figure 4 is a schematic curve diagram of the battery calendar (storage) life aging process, b in Figure 4 is a schematic curve diagram of the battery cycle life aging process, Figure 4c is obtained by fitting according to Figure 4a and Figure 4b, and c in Figure 4 This is a spliced diagram of the battery aging state curve in actual application scenarios. For any BMS working condition scenario, it can be decomposed into a cycle (work) + calendar (shelving/storage) life aging process. It is only necessary to obtain more comprehensive single-scenario battery life test data (for example, pure cycle at 25°C, pure rest at 35°C), and the battery aging state curve spliced through the above time connection method can approximately simulate the actual working scenario of the battery. Lifetime aging behavior, thereby achieving high-precision battery life prediction.

Optionally, the ambient temperature includes 25℃-60℃, and the storage time is 4h.

Specifically, by setting the threshold range of ambient temperature and storage time, large deviations in battery life estimation can be avoided. For example, when the battery is exposed to a low temperature of -30°C or a high temperature of 80°C, the service life of the battery will be significantly reduced. This causes the SOH aging curve to change too much, making the battery life estimation results inaccurate.

Figure 5 is a schematic structural diagram of a battery life prediction device according to an embodiment of the present invention. Referring to Figure 5, an embodiment of the present invention also provides a battery life prediction device. The battery life prediction device includes:

The aging state curve simulation module 201 is used to utilize the aging memory effect of the battery to be estimated to simulate the aging state curve corresponding to the battery to be estimated under different ambient temperatures and storage times.

The aging state curve splicing module 202 is used to splice the aging state curves according to time as a splicing basis to obtain the final aging state curve.

The battery life prediction module 203 to be estimated is used to obtain the life prediction result of the battery to be estimated based on the final aging state curve.

The above-mentioned battery life prediction device can execute the battery life prediction method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the battery life prediction method.

Continuing to refer to Figure 5, optionally, the aging state curve simulation module 201 is also specifically configured to store the battery to be estimated at the first ambient temperature according to the first storage time to simulate the first aging state curve; It is estimated that the battery is stored at the second ambient temperature according to the second storage time to simulate a second aging state curve.

Continuing to refer to FIG. 5 , optionally, the aging state curve splicing module 202 is specifically configured to splice the end of the first aging state curve to the starting end of the second aging state curve to obtain a time-continuous final aging state curve.

Continuing to refer to Figure 5, optionally, the battery life prediction module 203 to be estimated is also specifically configured to obtain the sum of the attenuation amounts of the battery to be estimated based on the aging state value corresponding to the end of the final aging state curve successively over time, thereby obtaining The battery life prediction results are to be estimated.

FIG. 6 shows a schematic structural diagram of an electronic device 10 that can be used to implement embodiments of the present invention. Electronic devices are intended to refer to various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (eg, helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are examples only and are not intended to limit the implementation of the invention described and/or claimed herein.

As shown in Figure 6, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores There is a computer program that can be executed by at least one processor. The processor 11 can perform the operation according to the computer program stored in the read-only memory (ROM) 12 or loaded from the storage unit 18 into the random access memory (RAM) 13. Perform various appropriate actions and processing. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via the bus 14. An input/output (I/O) interface 15 is also connected to bus 14 .

Multiple components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc. etc.; and communication unit 19, such as network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices through computer networks such as the Internet and/or various telecommunications networks.

Processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processing processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, for example, the battery life prediction method.

In some embodiments, the battery life prediction method may be implemented as a computer program, which is tangibly embodied in a computer-readable storage medium, such as the storage unit 18 . In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19 . When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the battery life prediction method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the battery life prediction method in any other suitable manner (eg, by means of firmware).

Various implementations of the systems and techniques described above may be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip implemented in a system (SOC), load programmable logic device (CPLD), computer hardware, firmware, software, and/or a combination thereof. These various embodiments may include implementation in one or more computer programs executable and/or interpreted on a programmable system including at least one programmable processor, the programmable processor The processor, which may be a special purpose or general purpose programmable processor, may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device. An output device.

Computer programs for implementing the methods of the invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that the computer program, when executed by the processor, causes the functions/operations specified in the flowcharts and/or block diagrams to be implemented. A computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, laptop disks, hard drives, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.

To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having a display device (eg, a CRT (cathode ray tube) or LCD (liquid crystal display)) for displaying information to the user monitor); and a keyboard and pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and may be provided in any form, including Acoustic input, voice input or tactile input) to receive input from the user.

The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., A user's computer having a graphical user interface or web browser through which the user can interact with implementations of the systems and technologies described herein), or including such backend components, middleware components, or any combination of front-end components in a computing system. The components of the system may be interconnected by any form or medium of digital data communication (eg, a communications network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

Computing systems may include clients and servers. Clients and servers are generally remote from each other and typically interact over a communications network. The relationship of client and server is created by computer programs running on corresponding computers and having a client-server relationship with each other. The server can be a cloud server, also known as cloud computing server or cloud host. It is a host product in the cloud computing service system to solve the problems of difficult management and weak business scalability in traditional physical hosts and VPS services. defect.

It should be understood that various forms of the process shown above may be used, with steps reordered, added or deleted. For example, each step described in the present invention can be executed in parallel, sequentially, or in different orders. As long as the desired results of the technical solution of the present invention can be achieved, there is no limitation here.

The above-mentioned specific embodiments do not constitute a limitation on the scope of the present invention. It will be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions are possible depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims (10)

1. A battery life prediction method, characterized by including: Utilize the aging memory effect of the battery to be estimated to simulate the aging state curve corresponding to the battery to be estimated under different ambient temperatures and storage times; The aging state curve is spliced according to time as the splicing basis to obtain the final aging state curve; The life prediction result of the battery to be estimated is obtained according to the final aging state curve. 2. The method according to claim 1, characterized in that, using the aging memory effect of the battery to be estimated, simulating the aging state curve corresponding to the battery to be estimated under different conditions includes: Put the battery to be estimated at the first ambient temperature for a first storage time to simulate a first aging state curve; The battery to be estimated is placed at a second ambient temperature for a second storage time to simulate a second aging state curve. 3. The method according to claim 1, characterized in that, splicing the aging state curve according to time as a splicing basis to obtain the final aging state curve includes: The end of the first aging state curve is spliced to the starting end of the second aging state curve to obtain a time-continuous final aging state curve. 4. The method of claim 1, wherein obtaining the life prediction result of the battery to be estimated based on the final aging state curve includes: According to the aging state value corresponding to the end of the final aging state curve successively in time, the sum of the attenuation amounts of the battery to be estimated is obtained, thereby obtaining the life prediction result of the battery to be estimated. 5. The method according to claim 3, characterized in that the aging state value at the end of the first aging state curve is equal to the aging state value at the beginning of the second aging state curve, The aging states of the batteries to be estimated corresponding to the same aging state value are different. 6. The method according to any one of claims 1 to 5, characterized in that the influencing factors of the aging memory effect of the battery to be estimated include: storage life or cycle life. 7. The method according to any one of claims 1 to 5, wherein the ambient temperature includes 25°C-60°C, and the storage time is 4 hours. 8. A battery life prediction device, characterized by comprising: The aging state curve simulation module is used to utilize the aging memory effect of the battery to be estimated and simulate the aging state curve corresponding to the battery to be estimated under different ambient temperatures and storage times; An aging state curve splicing module is used to splice the aging state curve according to time as a splicing basis to obtain the final aging state curve; The battery life prediction module to be estimated is used to obtain the life prediction result of the battery to be estimated based on the final aging state curve. 9. An electronic device, characterized in that it includes: one or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method as described in any one of claims 1-7. 10. A computer-readable storage medium with a computer program stored thereon, characterized in that when the computer program is executed by a processor, the method according to any one of claims 1-7 is implemented.